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Prediction of New Energy Vehicles via ARIMA-BP Hybrid Model

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1681))

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Abstract

An important measure of the development of the new energy vehicle market is the prediction of vehicle sales. It is of great significance to complete the construction of relevant supporting facilities, according to the predicted sales volume for the development of the Chinese new energy vehicle industry. Based on this, this paper proposes a combined model that organically combines a single prediction model. Firstly, the ARIMA model is used to predict the linear information in the sales data, and BP neural network model is used to predict the residual sequence between the previous prediction and the actual value. After that, it adds the prediction results to get the final prediction results of new energy vehicle sales. The results verified with the actual sales data show that the prediction accuracy of the ARIMA-BP Residual Optimization Combination model used in this paper is 85.07%. Compared with the single prediction model and the simple weighted combination prediction model, there are general advantages, which can be used for the actual monthly sales prediction of new energy vehicles.

Fund Project: This work was supported in part by the R&D Projects in Key Area of Guang-dong Province No. 2021B0101200003, National Natural Science Foundation of China under Grants No. 62072120, Guangdong Provincial Key Laboratory of Cyber-Physical System No. 2020B1212060069.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Beiteng Yang & Dongning Liu

  2. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Jianjun Liu

Authors
  1. Beiteng Yang
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  2. Jianjun Liu
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  3. Dongning Liu
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Corresponding author

Correspondence to Dongning Liu .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Yinzhang Guo

  4. Shanxi Datong University, Datong, China

    Xiaoxia Song

  5. Tongji University, Shanghai, China

    Hongfei Fan

  6. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  7. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

  8. Tongji University, Shanghai, China

    Bowen Du

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yang, B., Liu, J., Liu, D. (2023). Prediction of New Energy Vehicles via ARIMA-BP Hybrid Model. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_41

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  • DOI: https://doi.org/10.1007/978-981-99-2356-4_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2355-7

  • Online ISBN: 978-981-99-2356-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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